Performance measures of 8,169,869 examinations in the National Breast Cancer Screening Program in Taiwan, 2004-2020.

BACKGROUND: The benefits of mammographic screening have been shown to include a decrease in mortality due to breast cancer. Taiwan's Breast Cancer Screening Program is a national screening program that has offered biennial mammographic breast cancer screening for women aged 50-69 years since 2004 and for those aged 45-69 years since 2009, with the implementation of mobile units in 2010. The purpose of this study was to compare the performance results of the program with changes in the previous (2004-2009) and latter (2010-2020) periods. METHODS: A cohort of 3,665,078 women who underwent biennial breast cancer mammography screenings from 2004 to 2020 was conducted, and data were obtained from the Health Promotion Administration, Ministry of Health and Welfare of Taiwan. We compared the participation of screened women and survival rates from breast cancer in the earlier and latter periods across national breast cancer screening programs. RESULTS: Among 3,665,078 women who underwent 8,169,869 examinations in the study population, the screened population increased from 3.9% in 2004 to 40% in 2019. The mean cancer detection rate was 4.76 and 4.08 cancers per 1000 screening mammograms in the earlier (2004-2009) and latter (2010-2020) periods, respectively. The 10-year survival rate increased from 89.68% in the early period to 97.33% in the latter period. The mean recall rate was 9.90% (95% CI: 9.83-9.97%) in the early period and decreased to 8.15% (95%CI, 8.13-8.17%) in the latter period. CONCLUSIONS: The evolution of breast cancer screening in Taiwan has yielded favorable outcomes by increasing the screening population, increasing the 10-year survival rate, and reducing the recall rate through the participation of young women, the implementation of a mobile unit service and quality assurance program, thereby providing historical evidence to policy makers to plan future needs.

Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI.

BACKGROUND: Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning. PURPOSE: To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI). STUDY TYPE: Bicentric retrospective study. SUBJECTS: In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model. SEQUENCE: A 3 T, coronal, and sagittal images from T1-weighted proton density (PD) fast spin-echo (FSE) with fat saturation and T2-weighted FSE with fat saturation sequences. ASSESSMENT: The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet-53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images. STATISTICAL TESTS: Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two-way analysis of variance, Wilcoxon signed-rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists. RESULTS: The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025). DATA CONCLUSION: The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Current Progress of Platelet-Rich Derivatives in Cartilage and Joint Repairs.

In recent years, several types of platelet concentrates have been investigated and applied in many fields, particularly in the musculoskeletal system. Platelet-rich fibrin (PRF) is an autologous biomaterial, a second-generation platelet concentrate containing platelets and growth factors in the form of fibrin membranes prepared from the blood of patients without additives. During tissue regeneration, platelet concentrates contain a higher percentage of leukocytes and a flexible fibrin net as a scaffold to improve cell migration in angiogenic, osteogenic, and antibacterial capacities during tissue regeneration. PRF enables the release of molecules over a longer period, which promotes tissue healing and regeneration. The potential of PRF to simulate the physiology and immunology of wound healing is also due to the high concentrations of released growth factors and anti-inflammatory cytokines that stimulate vessel formation, cell proliferation, and differentiation. These products have been used safely in clinical applications because of their autologous origin and minimally invasive nature. We focused on a narrative review of PRF therapy and its effects on musculoskeletal, oral, and maxillofacial surgeries and dermatology. We explored the components leading to the biological activity and the published preclinical and clinical research that supports its application in musculoskeletal therapy. The research generally supports the use of PRF as an adjuvant for various chronic muscle, cartilage, and tendon injuries. Further clinical trials are needed to prove the benefits of utilizing the potential of PRF.

Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification.

Common pediatric distal forearm fractures necessitate precise detection. To support prompt treatment planning by clinicians, our study aimed to create a multi-class convolutional neural network (CNN) model for pediatric distal forearm fractures, guided by the AO Foundation/Orthopaedic Trauma Association (AO/ATO) classification system for pediatric fractures. The GRAZPEDWRI-DX dataset (2008-2018) of wrist X-ray images was used. We labeled images into four fracture classes (FRM, FUM, FRE, and FUE with F, fracture; R, radius; U, ulna; M, metaphysis; and E, epiphysis) based on the pediatric AO/ATO classification. We performed multi-class classification by training a YOLOv4-based CNN object detection model with 7006 images from 1809 patients (80% for training and 20% for validation). An 88-image test set from 34 patients was used to evaluate the model performance, which was then compared to the diagnosis performances of two readers-an orthopedist and a radiologist. The overall mean average precision levels on the validation set in four classes of the model were 0.97, 0.92, 0.95, and 0.94, respectively. On the test set, the model's performance included sensitivities of 0.86, 0.71, 0.88, and 0.89; specificities of 0.88, 0.94, 0.97, and 0.98; and area under the curve (AUC) values of 0.87, 0.83, 0.93, and 0.94, respectively. The best performance among the three readers belonged to the radiologist, with a mean AUC of 0.922, followed by our model (0.892) and the orthopedist (0.830). Therefore, using the AO/OTA concept, our multi-class fracture detection model excelled in identifying pediatric distal forearm fractures.

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Net (DCLU-Net). MATERIALS AND METHODS: A total of 297 participants who underwent of total of 303 MRI examination of the knee with fat-saturated proton density (PD) fast spin-echo (FSE) sequence in the sagittal plane were included. There were 214 men and 83 women, with a mean age of 37.46 ± 1.40 (standard deviation) years (range: 29-44 years). Of these, 107 participants had intact ACL (36%), 98 had partially torn ACL (33%), and 92 had fully ruptured ACL (31%). The DCLU-Net was combined with radiomic features for enhancing performances in the diagnosis of ACL tear. The different evaluation metrics for both classification (accuracy, sensitivity, accuracy) and segmentation (mean Dice similarity coefficient and root mean square error) were compared individually for each image class across the three phases of the model, with each value being compared to its respective value from the previous phase. Findings at arthroscopic knee surgery were used as the standard of reference. RESULTS: With the addition of radiomic features, the final model yielded accuracies of 90% (95% CI: 83-92), 82% (95% CI: 73-86), and 92% (95% CI: 87-94) for classifying ACL as intact, partially torn and fully ruptured, respectively. The DCLU-Net achieved mean Dice similarity coefficient and root mean square error of 0.78 (95% CI: 0.71-0.80) and 0.05 (95% CI: 0.06-0.07), respectively, when segmenting the three ACL conditions with pseudo data (P < 0.001). CONCLUSION: A dual-modules deep learning model with segmentation and classification capabilities was successfully developed. In addition, the use of semi-supervised techniques significantly reduced the amount of manual segmentation data without compromising performance.

Machine Learning Algorithm for Distinguishing Ductal Carcinoma In Situ from Invasive Breast Cancer.

PURPOSE: Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC). METHODS: In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. RESULTS: The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76-0.91), an AUC of 0.93 (95% CI, 0.87-0.95), a specificity of 0.75 (95% CI, 0.67-0.83), and a sensitivity of 0.91 (95% CI, 0.76-0.94). CONCLUSION: The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.